AI-900 Practice Test 8 — 10 Questions

Question 1

What is 'Azure AI Vision's colour analysis' and what information does it return?

Accepted Answer

Returning dominant colours, accent colour, and B&W detection for image theming and organisation. Azure AI Vision's color analysis extracts color information from images to support theming and organization tasks. It returns the dominant foreground and background colors, an accent color (the most vibrant color suitable for UI theming), and a boolean flag indicating whether the image is black-and-white. This is distinct from image editing or accessibility detection.

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Converting colour images to greyscale for accessibility or artistic purposes

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Adjusting image brightness, saturation, and contrast to optimise visual quality

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Detecting colour-related accessibility issues in user interface designs

Question 2

What is generative AI?

Accepted Answer

AI that creates new content such as text, images, or code based on learned patterns. Generative AI refers to models that learn patterns from training data and then produce new, original content—such as text, images, audio, or code—that resembles the training distribution. Unlike discriminative models that map inputs to labels, generative models (e.g., GPT, DALL-E) sample from a learned probability distribution to create novel outputs. This is the core definition tested in AI-900 for the 'features of generative AI workloads' domain.

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AI that classifies existing data into predefined categories

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AI that detects anomalies in structured data

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AI that controls physical robots

Question 3

A company deploys an AI system to screen job resumes. The system consistently rejects candidates from a certain university, but the company cannot determine which features led to the decision or how the model arrived at that outcome. Which Microsoft responsible AI principle is most directly violated?

Accepted Answer

Transparency. The scenario describes a system that makes decisions without providing any insight into how or why those decisions were made. Transparency, as a Microsoft responsible AI principle, requires that AI systems be understandable and that their decisions can be explained. Since the company cannot determine which features led to the rejection or how the model arrived at the outcome, the lack of explainability directly violates the Transparency principle.

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Fairness

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Accountability

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Privacy and security

Question 4

What is 'model cards' in responsible AI and what information do they contain?

Accepted Answer

Transparency documents describing a model's intended use, training data, performance, biases, and limitations. Model cards are transparency documents that accompany machine learning models to disclose their intended use, training data, performance metrics, known biases, and limitations. They are a key responsible AI practice, mandated by frameworks like Microsoft's Responsible AI Standard, to ensure stakeholders understand a model's capabilities and risks before deployment.

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Azure billing documents showing the monthly cost of running a model in production

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Technical specification sheets for AI hardware accelerators used in model training

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Playing cards used in gamification of AI training to motivate data labellers

Question 5

What are embeddings in the context of AI and language models?

Accepted Answer

Numerical vector representations of text that capture semantic meaning. Embeddings are dense numerical vector representations of text that capture semantic meaning, enabling language models to understand relationships between words and phrases. In the context of AI and language models, embeddings map words, sentences, or documents to high-dimensional vectors where similar meanings are closer in vector space, which is fundamental for tasks like semantic search, clustering, and transfer learning.

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The process of inserting AI capabilities into existing applications

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The training dataset used to build a language model

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Compressed versions of large language models for edge deployment

Question 6

What is the purpose of Azure AI Vision's 'thumbnail generation' feature?

Accepted Answer

Generating crop-focused preview images that highlight the most important content area. Azure AI Vision's thumbnail generation feature analyzes the image content to identify the most important region (e.g., a person's face or a prominent object) and then crops the image around that region to produce a focused preview. This is distinct from simple resizing or compression, as it uses AI-based spatial analysis to preserve the key subject while discarding irrelevant background areas.

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Reducing file sizes of images for faster web page loading

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Creating thumbnail-sized AI model icons for the Azure portal

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Generating multiple image variations in different artistic styles

Question 7

What is 'Azure OpenAI's content filter' configurability and why does it matter?

Accepted Answer

Adjustable severity thresholds per harm category for legitimate domain-specific use cases. Azure OpenAI's content filter configurability allows administrators to adjust severity thresholds for each harm category (e.g., hate, violence, self-harm) to accommodate legitimate domain-specific use cases, such as medical or legal content that may require higher tolerance. This matters because it balances safety with utility, enabling organizations to fine-tune filtering based on their unique content policies and compliance needs without blocking valid applications.

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Configuring which users can access Azure OpenAI based on their location

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Setting the maximum token count before content is filtered for length

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Configuring which Azure OpenAI models are available to different teams within an organisation

Question 8

Which Azure AI service allows you to analyze medical images for clinical decision support?

Accepted Answer

Azure AI Health Insights and specialized medical imaging AI. Azure AI Health Insights includes specialized medical imaging AI capabilities designed to analyze radiology and other medical images for clinical decision support. This service is built on domain-specific models trained on medical data, unlike general-purpose computer vision services.

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Azure AI Custom Vision with medical training data

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Azure AI Translator for medical documents

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Azure AI Speech for dictation only

Question 9

What is the purpose of key phrase extraction in Azure AI Language?

Accepted Answer

To identify the main talking points and important concepts in text. Key phrase extraction in Azure AI Language is designed to identify the main talking points and important concepts within a given text. It analyzes the text structure and returns a list of key phrases that summarize the core topics, enabling quick understanding of the document's primary themes without reading the entire content.

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To translate key phrases between languages

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To classify text into positive or negative sentiment

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To generate new text based on key topics

Question 10

What is 'personalisation' as an AI workload and how does it differ from recommendation?

Accepted Answer

Dynamically adapting the full user experience for each individual based on their real-time behaviour. Personalisation as an AI workload involves dynamically adapting the full user experience—such as content, layout, or interactions—for each individual based on their real-time behaviour and historical data. This goes beyond simple recommendation by modifying the entire interface and flow, not just suggesting items. It leverages machine learning models that continuously learn from user actions to tailor the experience.

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Allowing users to customise the visual theme and layout of an application manually

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Recommending specific items a user might purchase based on their purchase history

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Creating personalised data privacy policies for each user based on their location